Description
Study of the interdependence among economies is of considerable importance. This area
includes issues such as the increasing importance of regional economic interactions, the effects of
economic growth and recession in the advanced economies on emerging market countries, and
financial contagion. A wide range of related terms and methodologies are used in the literature of
interdependence. The purpose of this paper is to review the major concepts and various measurements
of interdependence in financial markets and the real economy, serving as a reference and benchmark
for future research on interdependence among specific regional or global economies
Journal of Financial Economic Policy
Measuring macroeconomic and financial market interdependence: a critical survey
Linyue Li Nan Zhang Thomas D. Willett
Article information:
To cite this document:
Linyue Li Nan Zhang Thomas D. Willett, (2012),"Measuring macroeconomic and financial market
interdependence: a critical survey", J ournal of Financial Economic Policy, Vol. 4 Iss 2 pp. 128 - 145
Permanent link to this document:http://dx.doi.org/10.1108/17576381211228989
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Economic Policy, Vol. 4 Iss 2 pp. 146-159http://dx.doi.org/10.1108/17576381211228998
Paul Simshauser, Tim Nelson, (2012),"The second-round effects of carbon taxes on power
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dx.doi.org/10.1108/17576381211228970
Sowmya Dhanaraj, Arun Kumar Gopalaswamy, Suresh Babu M, (2013),"Dynamic interdependence
between US and Asian markets: an empirical study", J ournal of Financial Economic Policy, Vol. 5 Iss 2 pp.
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Measuring macroeconomic and
?nancial market
interdependence: a critical survey
Linyue Li
Claremont Institute for Economic Policy Studies, Claremont, California, USA
Nan Zhang
Claremont Institute for Economic Policy Studies, Claremont, California,
USA and
Milken Institute, Santa Monica, California, USA, and
Thomas D. Willett
Claremont Institute for Economic Policy Studies, Claremont, California, USA
Claremont Graduate University, Claremont, California, USA and
Claremont McKenna College, Claremont, California, USA
Abstract
Purpose – Study of the interdependence among economies is of considerable importance. This area
includes issues such as the increasing importance of regional economic interactions, the effects of
economic growth and recession in the advanced economies on emerging market countries, and
?nancial contagion. A wide range of related terms and methodologies are used in the literature of
interdependence. The purpose of this paper is to review the major concepts and various measurements
of interdependence in ?nancial markets and the real economy, serving as a reference and benchmark
for future research on interdependence among speci?c regional or global economies.
Design/methodology/approach – Major measurements of interdependence are reviewed from
simple approach to more complicated ones, and strengths and weaknesses of the various
measurements of interdependence are discussed.
Findings – This paper surveys the various major measurements of interdependence and illustrates
how they have been used to address a substantial range of issues.
Originality/value – The paper shows that studies of macroeconomic and ?nancial interdependence
use the same types of econometric measurements. The review and critiques of these various types of
measures should be of value to those wishing to do research in these areas and also to those wishing to
have a better understanding of papers that they read.
Keywords Macroeconomics, Financial markets, Econometrics, Interdependence, Contagion,
Financial crisis, Econometric measurements
Paper type Research paper
1. Introduction
The rapid increase in international trade and capital ?ows associated with
globalization has generated substantial interest in issues of ?nancial and economic
interdependence. These are often discussed under the labels of the international
transmission mechanisms, business cycles and stock market synchronization,
decoupling and recoupling, and international contagion.
The global ?nancial crisis drew even more attention to the subject as the impacts of US
subprime crisis on the world economies have providedevidences of global interdependence.
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1757-6385.htm
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Journal of Financial Economic Policy
Vol. 4 No. 2, 2012
pp. 128-145
qEmerald Group Publishing Limited
1757-6385
DOI 10.1108/17576381211228989
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Following the US subprime crisis, the ?nancial markets and real economies in many
advanced and emerging market countries were hard hit. Many ?nancial institutions
sufferedhuge losses due to their massive exposure to the subprime loans in2008 andthe US
stock market collapsed. The Dow Jones Industrial Average dropped 18.1 percent and the
S&P 500 fell more than 20 percent within one week in October after Lehman brothers ?led
for bankruptcy. The turmoil in the US ?nancial market triggered the global crisis. Many
international stockmarkets experiencedtheir worst short termdeclines intheir history with
drops of around 10 percent in many indices within one day in October 2008.
Initially it was believed that the adverse effects on economic growth would be
largely limited to the USA and Europe whose banks were most affected, but as these
economies fell into recessions, their large drops in imports began to hit heavily much of
the rest of the world (for discussion of the slow spread of the crisis, see Willett et al.
(2010)). While US output growth fell to zero in 2008 and to 22.6 percent in 2009, world
output growth in 2009 also turned negative in 2009 (International Monetary Fund,
2010). Many had argued that with their rapid growth and the increase in intra regional
interdependence, many of the emerging market economies (EMEs) had largely
decoupled from the advanced economies. The crisis showed that this was overstated
and highlighted the importance of careful empirical studies of global economic
interdependence.
The study of ?nancial and economic interdependence among countries and regions
includes research not only under this speci?c title, but also under related terms such as
business cycle synchronization, recoupling/decoupling, the extent of ?nancial and
economic integration, contagion, and so on. Most studies concentrate on a limited number
of aspects of interdependence. Many of these aspects are closely related, however, and
make use of the same types of measurement techniques such as correlation analysis,
vector auto-regression (VAR) analysis (impulse response functions), and factor analysis.
This paper focuses on the interrelationships among these different sets of studies,
outlines the various major types of measurement techniques used and discusses their
strengths and weaknesses. These are illustrated with reviews of a number of recent
studies of major types of issues such as business cycle synchronization, and linkages
such as contagion.
2. Channels and concepts of interdependence
Economic interdependence is a broad term covering the whole range of ways in which
the behavior of variables such as stock market movement, interest rate, economic
growth, etc. is in?uenced by developments in other economics. We can think of greater
economic integration or globalization through international trade and capital ?ows as
leading to greater international transmission of developments in one country to other
countries and thus generating greater economic interdependence.
If one country goes into recession, it will decrease its imports from other countries.
Its trading partners thus face reduction in their exports which in turn reduces their
economic growth. The large reduction in the exports of developing countries caused by
the recession in the advanced economies that resulted from their recent ?nancial crises
is an important example. Obviously, the greater are trade ?ows among countries, the
greater those effects will be.
Likewise, international capital ?owlink ?nancial and economic developments in one
countrytothose inother countries. For example, bothlower interest rates inthe advanced
Measuring
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economies and increased economic growth in EMEs increase the incentives for larger
capital ?ows to the latter. This in turn may make these countries vulnerable to a sudden
stop of these ?nancial ?ows as we have seen in a number of crises.
Recent research has found that not only the levels but also the composition of trade
and capital ?ows can have a large effect on the international transmission of
such developments. For example, short term bank loans and portfolio investment are
much more susceptible to contagion effects than are ?ows of direct investment[1].
Likewise, empirical studies have found that intra-industry trade plays a major role in
increasing business cycle transmission, while inter-industry trade tends to reduce
macroeconomic interdependence due to sector speci?c shocks (for analysis and
references, see Li (2011)).
Below we brie?y discuss the topics of business synchronization among
economies, the debate over decoupling and recoupling, and the spread of ?nancial
contagion.
2.1 Business cycle synchronization
In the classic de?nition by Burns and Mitchell (1946), business cycle synchronization
occurs when:
[. . .] a cycle consists of expansions occurring at about the same time in many economic
activities, followed by similarly general recessions, contractions, and revivals which merge
into the expansion phase of the next cycle.
According to their de?nition:
[. . .] this sequence of changes is recurrent but not periodic; in duration business cycles vary
from more than one year to ten or twelve years; they are not divisible into shorter cycles of
similar character with amplitudes approximating their own.
Business cycles are usually measured by GDP growth rate, domestic consumption
growth rate, domestic investment growth rate, employment rate, and in?ation. Studies
focus on both the strength of the relationships and the factors that in?uence them. One
major topic is the effect of trade ties on synchronization measures. These relationships
are important for issues of macroeconomic policy coordination and are one of the
major criteria developed in the literature on optimum currency areas for determining
the costs and bene?ts of joining a common currency area[2].
2.2 Recoupling/decoupling
In recent years there has been considerable debate about the extent to which emerging
market countries are decoupling or recoupling with the advanced economies. While
increasing globalization would be expected to generate stronger coupling among
economies, some have argued that the more rapid growth in EMEs and increases in
regionalization would lead to a decoupling of their economic growth and stock market
performance from developments in the advanced economies[3]. In this view, while
economic interdependence has been increasing among some sets of countries, it has
been decreasing among others. As decoupling implies a break in a relationship that
was previously closely linked, this de?nition lends itself naturally to discussions of
changes in patterns of comovements or correlations.
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2.3 Contagion (or spillover)
Contagion is usually referring to a spread of a crisis from one economy to another. The
term of contagion is analogous to the spread of contagious diseases and became
particularly popular since the Mexico peso crisis in the end of 1994.
Contagion implies the existence of interdependency but there can be a number of
different types of interdependency and these can have very different implications for
policy[4]. As a result, the term contagion requires one to specify what type in order to
communicate effectively. In public discussions, there is often a connotation that
contagion is due to irrational panic and thus re?ects a failure of markets to operate
ef?ciently. This is far from the only possible type of contagion, however. In its broadest
sense, contagion can refer to any spread of economic developments in one country to
other countries. In this sense, it is the same as interdependence. More commonly,
contagion is used to refer to stronger than normal interrelationships associated with
crises. One popular de?nition of contagion is that a crisis in one country increases the
risk of crises inother economies. This type is measuredbyestimatingthe extent to which
a crisis in one country increases the probability of crises in others from equations that
control for a number of factors such as current account de?cits, low international
reserves and rapid credit growth that are often associated with crises[5].
For looking at effects on variables such as interest rates and stock prices, continuous
measures are used. A typical de?nition of this more restricted view of contagion is the
transmission of shocks to other countries or the cross-country correlation beyond any
fundamental link among the countries and beyond common shocks (which is also called
the “pure contagion”). This de?nition is usually referred to as excess comovements,
commonly explained by herding behavior. This is often measured by looking at the
increase in cross-country correlations increase during “crisis times” relative to
correlations during “tranquil times”.
Interpretations of the causes of the particular crises vary widely. In large part this is
because there are often multiple causes and these can vary from one case to another.
Thus, careful empirical and case studies are essential to shed light on such episodes.
3. Measurements of interdependence
The literature provides many methods to measure various aspects of interdependence.
Here we discuss a number of the most widely used measures: correlation, cointegration,
panel, VAR, and dynamic factor analyses[6]. Some research uses the combination of
more than one of the above methods.
Table I summarizes the main measurements of interdependence discussed in this
paper. These are discussed beginning with the simplest approach, correlation analysis,
and moving on to more complicated ones and from single variable to multiple variable
analyses.
3.1 Correlation or comovement analyses
Correlation or comovement analysis is one of the most widely used measurements in
recent interdependence or contagion literature. It includes static and dynamic analysis.
It is important to remember that correlation need not imply causation, nor does it
measure only the degree of interdependent among variables. Correlations are
often in?uenced by common shocks. Furthermore, even with country speci?c shocks
correlations across countries will often vary with the nature of the shock.
Measuring
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p
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(
c
o
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t
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u
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d
)
Table I.
Summary of
measurements of
interdependence
JFEP
4,2
132
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.
(
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Table I.
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As a result the correlations among asset prices used for the allocation of investment
portfolios and among economic growth rates used to look for patterns and trends in
macroeconomic interdependence often vary considerably over time.
3.1.1 Static correlations.
(a) Simple correlation. The simple correlation measures the overall comovements
and serves as the basic framework for a quick assessment of interdependence. It is
obtained by dividing the covariance of the two variables by the product of their
standard deviations[7].
Many researchers have used correlation analysis to test for interdependency or its
related terms. For example, de?ning contagion as a signi?cant rise in the correlation
among asset returns, Baig and Goldfajn (2001) test for evidence of contagion between
the ?nancial markets of Asian countries during the crisis of 1997-1998 and ?nd that
correlations for currency and sovereign spreads increase signi?cantly while the equity
market correlations offer mixed results.
The advantage of the simple correlation is its straightforwardness and intuitiveness.
However, it has important limitations. One of the most important limitation is that as
noted above correlations are the product of both the sensitivity of developments in one
country to those in another (interdependence) and those developments themselves.
Thus, a high correlation could occur because of a common shock. This explains a
substantial portion of the increases in the correlation of growth rates among Asian
economies during their ?nancial crisis of 1997-1998 and among many of the advanced
economies during the ?nancial crisis of 2007-2009 (Willett et al., 2010; Zhang, 2011a).
Different patterns of shocks within the economies in question can also lead to
substantial differences incorrelations. Thus, we often see correlations varysubstantially
over time. Zhang (2011a) ?nds this for stock returns of the USA and Asian economies.
Correlations almost always rise during crises. The frequent instability of correlations
suggests that we should be careful not to misinterpret short run variations as the
beginning of long run trends.
One controversial aspect of using changes in correlations to measure contagion
concerns how to test for statistical signi?cance. Forbes and Rigobon (2001, 2002), for
example, criticize that the simple correlation is biased in the analysis of contagion
because of the presence of higher volatility in market returns in the crisis periods leads
to increased correlations. Correcting for the heteroskedasticity problem, they ?nd
virtually no evidence of contagion during the multiple crises and that the greater
degree of comovement of the stock market during the crisis period may simply re?ects
a continuation of the trend in market interdependence. However, other researchers
argue that the increase in variance is a normal part of a crisis and so adjusting for these
when testing signi?cance may not be appropriate (Baig and Goldfajn, 2001).
One important issue that studies often do not address suf?ciently is the length of the
time periods over which correlations should be measured. Appropriate lengths can
vary substantially depending on the speci?c issue being addressed. For example,
managers of investment funds attempting to beat the market may be interested in
correlations over very short time periods, while considerably longer time periods
would be relevant for issues of macroeconomic policy coordination[8].
A related issue with simple correlations is that they do not distinguish long run
relationships, i.e. trends, from short run movements around these trends. In general
macroeconomic interdependence will have a larger impact on these shorter term
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movements than on the trends themselves, although of course the extent to which an
economy is opened to the world economy can affect its growth rate[9]. An obvious way
of dealing with this problem is to look at the correlations of deviations from trends.
(b) Trend-?ltered correlations (linear and non-linear trend). The trend-?ltered
correlations remove the effects of medium or long term trends (linear or non-linear) and
?nd the comovements on detrended data. Generally speaking, there are two categories
of de-trending methods, linear and non-linear. For linear de-trending techniques,
ordinary least squares (OLS) regression is often used to estimate a linear trend-line. For
non-linear de-trending techniques, the Hodrick-Prescott (HP) ?lter is often selected.
Willett et al. (2011), for example, calculate both the linear detrended and HP ?lter
detrended correlations between the growth rate of US and several EMs. Because
economies such as China and India continue to have high growth rates, it is often
conclude that they are little affected by the global ?nancial crisis. When one adjusts for
their high trend growth rates, however, it is found that they suffer declines on the same
order as the advanced economies. Thus, while simple analysis supports the decoupling
hypothesis, more careful analysis ?nds the supports the opposite conclusion.
The HP ?lter smoothes data with a procedure of squared error minimization and then
removes short-term ?uctuations[10]. A main drawback of the HP ?lter is the end-point
problem. The calculation puts more weight on the observations at the end of the series
(Marinheiro, 2005). But if the studyhas relatively large numbers of observations and focus
on mostly the middle points, the biases are limited. Although the simple correlations and
the static trend-?ltered correlations are straightforwardandeasyto calculate, theyare less
convenient to capture high frequent time varying or dynamic characteristics of
the comovements which are often shown in the fast-changing ?nancial markets. The
dynamic correlation methods can solve this problem by providing dynamic solutions.
3.1.2 Dynamic correlations. Dynamic correlations provide time-varying correlations
between economic variables. Some examples are the dynamic conditional correlations
(DCC)-GARCH-developed by Engle (2002) and the time varying coherence functions
(TVCF) used by Essaadi and Boutahar (2008).
(a) Dynamic conditional correlations-GARCH. DCC-GARCH developed by Engle
(2002) takes the volatility or heteroscedasticity, and autocorrelation of the variables
into account to produce a time-varying calculation of correlations. It is estimated in a
two-stage procedure. First, univariate GARCH models are ?tted for each of the
variables in the speci?cation. Then using transformed residuals resulting from the ?rst
stage, the DCC estimators are estimated[11].
This method has then been widely used in the research on contagion. For example,
Wang and Thi (2006) use it to examine the impact of the 1997-1998 Asian ?nancial
crises on the Chinese economic area and ?nd positive correlation coef?cients of stock
returns. The International Monetary Fund (2008) in its Global Financial Stability
Reports uses DCC-GARCH to analyze the comovements in stock markets between the
USA and some global emerging market regions as a whole and ?nd varied but overall
increasing correlation levels during the past several years up to 2008. A study by
Zhang (2011a) calculates DCC for the stock returns of some Asian economies and the
USA during the recent ?nancial crisis and ?nds that the correlations of Asian equity
markets with the USA have tended to increase over time, but that there was a
decrease in correlations during the beginning of the recent crisis ( July 2007-August
2008), then a substantial increase after the collapse of Lehman brothers up to late 2009
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(September 2008-August 2009), and a small decrease in the later period of the recent
crisis (September 2009-August 2010). Much of the decoupling debate wrongly focused
on these short run changes as if they re?ected changes in longer term trends.
DCC-GARCH method has proven to be more robust than the static correlation
models, especially for looking at ?nancial variables which often face greatly changing
volatility. DCC-GARCH has limitations, however. For example, the parameters of the
DCC-GARCH model assume that errors are normally distributed may be greatly
affected by outliers. While the normality assumption ?ts the actual behavior of
?nancial markets fairly well during calm periods, it breaks down badly during crisis
periods which display much larger changes than predicted by the normal distribution.
This is a major problem for many of the risk models used by the ?nancial sector[12].
(b) Time varying coherence functions. The time varying coherence analysis is
another example of dynamic methods of comovement analysis. Essaadi and Boutahar
(2008), use this approach[13] to estimate TVCF for non-stationary time series to capture
both degrees of comovements and their behavior in each frequency[14]. Their research
?nds that there is a common business cycle in East Asia, especially after the 1997-1998
crises.
The main advantage of the frequency approach TVCF is that it not only detects
comovement dynamics in different cycles but also identi?es changes in synchronization
processes at different frequencies. In addition, the frequency approach does not depend
on any particular detrending technique and does not have the “end-point” problems.
3.2 Cointegration analyses
Cointegration tests capture one aspect of the integration relationships among
economies. Mathematically, if some linear combination of two or more series such as
in?ation in two countries has a lower order of integration, the series are considered
cointegrated. Let us explain.
Empirical macroeconomic studies frequently involve variables with trends such as
the money supply, price level, and aggregate economic growth and some of its
components such as consumption and investment. Such series are often non-stationary.
Regression of one of such variables on another would be misleading since much of the
correlation would be due to common trends. Thus, simple regression relationships could
be spurious. To manipulate these series appropriately, the procedure of taking ?rst
differences I(1), or second differences I(2), or other transformations (such as seasonal
adjustment) is used to reduce them to stationarity (Greene, 2008). Thus, for example,
while the price level has a strong trend, its ?rst differ, the in?ation rate, may not.
In?ation may also have a strong trend than it would be differenced again.
With theory testing, an important issue with such differencing is whether the
predicted relationships would still hold up in difference form. Thus, while we would
expect money growth and in?ation to be correlated, the expected relationship between
money growth and the ?rst difference of in?ation is unclear relationships.
Generally speaking, if two time series are integrated to different orders, linear
combinations of themwill be integrated to the higher of the two orders. If both series are
each drifting upward with their own trend, then the difference between themshould also
be growing, with yet another trend, unless there is some relationship between those
trends. For example, if the two series are both I(1), then there may be a vector of
parameters such that the disturbances are I(0) (i.e. a stationary, white noise series).
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Intuitively, this phenomenon would imply that the two series are drifting together at
roughlythe same rate. If the two series satisfy this requirement, they are considered to be
cointegrated (Greene, 2008).
In this case, we can distinguish the long-run relationship between the two series, that
is, the manner in which the two variables move upward together, and the short-run
dynamics, that is the relationship between deviations of each series from its long-run
trend. If there exist cointegration relationship between two time series, transforming
them to stationary data through 1st difference or 2nd difference procedure would hide
the long-run relationship between the two time series. Thus, the cointegration test is
usually used to analyze the long-run relationship between the economic variables when
all the variables are found to be non-stationary (i.e. there exist unit roots).
Error correction estimation is often used to investigate the short-run interactions of
the economic series such as growth rates or stock markets. As an example, Karolyi and
Stulz (1996) break down the comovement of stock markets between the USA and Japan
into long-run and short-run linkages[15].
Cointegration tests are often used both in studies of ?nancial and macroeconomic
integration and contagion. Burdekin and Siklos (2011), for example, examine long-run
cointegrating relationships for the Asian economies and the USA during 1999-2010 and
apply quantile regression techniques[16] to allow for variation over the spectrum of the
return distributions. They ?nd that the enormous growth of the Shanghai market in
the new millennium has been accompanied by substantial integration with other
regional and world market. The major advantage as well as limitation of the
cointegration method is that it is only suitable to analyze the long-run relationship
between non-stationary economic variables.
3.3 Panel analyses
Panel data sets combine time series and cross section data. Thus, one is able to
investigate relationships both across countries and markets and over time (Greene,
2008). A variety of different models for panel data can be constructed. Broadly, they
fall into the following three categories:
(1) Pooled regression. If individual effect contains only a constant term, the OLS
provides consistent and ef?cient estimates of the parameters.
(2) Panel regression with ?xed effects. Individual effects can be modeled as
coef?cients on individual-speci?c binary variables. Most economists favor
using “?xed effects because this form of unobserved heterogeneity can be either
correlated with regressors or uncorrelated with them, just as any other
regressors can be” (Greene, 2008).
(3) Panel regression with random effects. Some models make the strong assumption
that individual effects are “random” in the sense that they must be uncorrelated
with all regressors. In this form of setting, unobserved heterogeneity affects the
residuals in the equation of interest.
The major difference between ?xed and random effects is whether the unobserved
individual effect embodies elements are correlated with the regressors in the model, not
whether these effects are stochastic or not. The ?xed effects estimator of the slope
parameters is consistent even if the true model is pooled or with random effects,
because the ?xed effects model allows individual effects to be correlated with other
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explanatory variables but it does not require the variables to be correlated. The
random effects model is not consistent if the true model is ?xed effects model, because
the random effects model imposes “no correlations between the individual effect and
the other explanatory variables” (Greene, 2008). In addition, the random effects model
allows the identi?cation of the marginal effects of time-invariant regressors to avoid
“perfect multicollinearity” when the ?xed effects model is applied, because the
individual effect is just a shock in the random effect model, but the estimates are
consistent only if the strong assumptions underlying the random effects are valid
(i.e. no correlations between the individual effect and the other explanatory variables).
Yeyati (2011) provides an example of the use of pooled panel regression to
investigate the decoupling of the main emerging markets from the advanced economies
in both ?nancial and real terms. The panel regressions for the annual growth rate of
country’s cyclical output (relative to a log linear GDP trend) on the G7 and Chinese
cycles for the periods of 1993-1999 and 2000-Q3 2010 show that the growth of some
emerging markets are becoming less coupled with the advanced economies and more
coupled with China.
Baur and Fry (2008) apply a panel ?xed time effects model to equity returns for
11 Asian economies during the Asian crisis of 1997-1998 and ?nd that interdependencies
are substantially more important than contagion during the crisis. The ?xed time effects
are interpreted in comparison to a base period and are assumed to re?ect contagion.
Actually, the ?xed time effects can capture movements across all asset markets that are
not explained by regional or global factors. Baur and Fry address that system-wide
contagion exists if the value of the ?xed time effect is greater than a threshold based on
conventional (99 percent) signi?cance levels.
Using high signi?cance requirements is certainly correct for scienti?c analysis
where we should require strong evidence to accept a hypothesis. From a policy
perspective, however, such stringent requirements are questionable. They implicitly
assume no contagion as the base presumption. Signi?cance from the standard types of
tests at say the 30 percent level would still suggest that the odds were 70-30 that there
was some contagion and policy makers would not have the luxury of waiting for a
larger number of observations before making decisions.
The International Monetary Fund (2008) in its Financial Stability Report investigates
the spill-over effects in equity markets fromthe advanced economies to EMEs fromMay
2008 to January 2011 using a ?xed effect panel analysis. The estimation on Asian equity
markets, for example, suggests that the global factors are statistically signi?cant: equity
prices are positively associated with global excess liquidity and negatively with credit
and market risk prime. For domestic factors, GDP growth, an expected exchange rate
appreciation, and an increase of market capitalization have positive effects on equity
prices, while interest rate differentials have a negative effect.
The fundamental advantage of panel analysis over a cross section is that
researchers have great ?exibility in modeling differences in behavior across individual
units and their dynamics. Of course panel analysis requires a larger set of variables
than correlation or cointergration tests.
3.4 Vector auto-regression (VAR) analyses
VAR analysis takes endogeneity of different economic variables into account when
investigating interdependencies among economies. It analyzes the dynamic impact
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of random disturbances and describes the evaluation of a set of endogenous variables
in the system as a linear function of their past evolution[17]. VAR models are usually
presented with impulse response functions that measure the effects of the different
shocks in one variable on the other variables, and variance decompositions that
measure the relative importance of the different shocks to the variation in the different
variables. Granger causality tests are often used in the VAR analysis to decide the
endogeneity of the variables. These disclose statistical but not necessarily behavioral
causality.
Kimet al. (2009), for example, investigate the degree of real economic interdependence
between nine emerging Asian countries and major industrial countries including Japan
and the USA. They document the evolution of macroeconomic interdependence for the
emerging Asian economies through changing trade and ?nancial linkages at both
the regional level and the global level. They apply a panel VAR model to estimate the
degree of real economic interdependence measured by aggregate output growth rates
before and after the 1997-1998 Asian crises. Their empirical ?ndings show that real
economic interdependence increased signi?cantly in the post-crisis period, indicating
“recoupling”, rather than decoupling.
The International Monetary Fund (2007) in its WEO report also uses the VAR
method to examine the spillover of the USA to other 130 economies in GDP and ?nds
that in general, the spillovers from growth in the USA are signi?cantly higher in the
post-1987 half of the sample (1970-2005). This suggests that perceived large declines in
the macroeconomic importance of the USA have been overstated and that the rapidly
increasing trade intergration in many regions has not undercut the importance of
global interdependence.
Using a structural VAR in the generalized method of moments (GMM) model[18],
Angkinand et al. (2010) ?nd an increase in interdependence between advanced country
stock market returns over time and that the spillover effects from the USA to other
industrial countries are particularly large during the recent ?nancial crisis. A study by
Zhang (2011b) investigates the impact of US stock market movements on Asian
markets during the recent ?nancial crisis using VAR analysis and ?nds that global
factors, especially the US equity market, also effect Asian equity markets more
strongly during the crisis.
The major advantage of the VAR method is that it analyzes the effects of shocks
allowing for interactions among variables and provides dynamic estimates. The VAR
method provides a systematic approach to imposing restrictions[19] and to de?ne
endogeneity among variables and capture relationships which are often hidden to
standard procedures such as OLS regressions. A limitation of the method is that the
robustness of the VAR estimations depends on a plausible setup on the endogenous
assumptions among variables. Another limitation of the VAR approach is that as it has
to be estimated with limited number of variables, all effects of omitted variables will be
in the residuals. This may lead to major distortions in the impulse responses, making
structural interpretations more dif?cult.
3.5 Dynamic factor analyses
Dynamic factor analysis is a technique used to detect common patterns in a set of time
series and relationships between these series and explanatory variables[20]. Taking
the model used by Kose et al. (2008a, b) as an example, dynamic factor analysis
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characterizes the degree of synchronization over time in various dimensions (global
factor, regional factors, country factors, and idiosyncratic factors) without making
strong identifying assumptions to disentangle different types of common shocks[21].
The dynamic relationships in the model are captured by modeling each factor and
idiosyncratic component as an autoregressive process. This simultaneously picks up in
a ?exible manner, the contemporaneous spillovers of shocks as well as the dynamic
propagation of business cycles without putting a priori restrictions on the structure of
the propagation mechanism or the directions of spillovers[22]. A surprising conclusion
of their analysis is that contrary to what would be expected from increased
globalization, the global factor was less important in the second period.
Li (2011) adds additional macroeconomic variables such as exports and imports to
Kose et al.’s model and also ?nds that the world factor has become less important in
explaining the macroeconomic ?uctuations from sub-period 1961-1984 to sub-period
1985-2007. Li also ?nds that contrary to perceptions of increased regionalization,
regional factors do not play an important role in explaining aggregate volatility except
for consumption. The explanatory power of country factors increases as variances are
driven more by country and idiosyncratic factors than by the world factor[23]. This
con?icts with the results from studies of output growth ?uctuations. The cause of these
differences is an important area for further research.
A static factor model provides a description of the variance-covariance matrix of a
set of random variables, while a dynamic factor model provides a description of the
inter-periods correlations. Therefore, the dynamic factors can describe
contemporaneous and temporal covariance among the variables (Kose et al., 2008a, b).
Compared with correlation approaches, an important advantage of the dynamic
factor model is that it allows for the separation of idiosyncratic components and common
comovements of global, regional, country-speci?c, and idiosyncratic factors. It is well
suited to investigate the degree of region-wide comovements and to study the joint
properties of ?uctuations in output and its components. However, the disadvantage of
dynamic factor models is that they need relatively long time series and it is easy to lose
degrees of freedom. In addition, it cannot be used to analyze bilateral comovements
between concerned countries.
4. Concluding remarks: the consistency of measurements
Empirical studies of ?nancial and economic interdependence have provided
considerable useful information. For example, they ?nd that most national economies
and ?nancial markets are substantially in?uenced by international in?uences, but also
that these are generally not as strong as to completely dominate the national
performance. Often such studies ?nd that reality lies well within the range of the extreme
popular opinions offered about such issues as decoupling and recoupling and contagion.
However, there is still considerable disagreement among experts about the strengths of
some important forms of interdependence.
It is not surprising that different studies often do not exactly agree. More troubling is
that they sometimes fundamentally disagree. In part this is no doubt because some
experts have strong beliefs about the nature of certain relationships and interpret their
results in this light. But we also ?nd that not only the use of different sets of countries
and time periods but also different estimation techniques sometimes yield substantially
different conclusions[24].
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The differences in results from different data sets should remind us that economic
relationships are generated by the behavior of human beings and thus can vary from
one situation to another. While we are able to capture some fairly strong regularity,
these often do not have the consistency of the physical laws of nature. The differences
from identical data sets resulting from different estimation techniques such as were
illustrated at the end of the previous section need to be the focus of extensive research.
In the meanwhile where alternative approaches have been recommended we should
check the sensitivity of the results to the different methods. Obviously we should have
more con?dence when the results of doing this roughly agree than when they differ
substantially.
Notes
1. See the analysis and references in Sula and Willett (2009) and the companion paper in part
one of this special issue by Efremidze et al. (2011).
2. See, for example, the general discussion of OCA criteria and references in Willett (2003a, b)
and applications to Asia and Europe in Willett et al. (2010a, b).
3. For analysis and references, see Willett et al. (2010).
4. For further more discussion and references, see Liang and Willett (2008).
5. See, for example, Willett et al. (2005) and Eichengreen et al. (1996).
6. Some of the other techniques used are probit analysis for studying the probability of
effects of the spread of crises (Angkinand et al., 2009) and principal components analysis
for identifying the common factors of the spread of the crisis (for detailed analysis, see
Forbes and Rigobon (2001)). They are not necessarily completely exclusive from methods
we discuss in this paper though. For example, the probit method is often used in panel
analysis.
7. The mathematical presentation of the simple correlation is as the following:
r
X;Y
¼ corrðX; YÞ ¼
cov ðX; YÞ
s
X
s
Y
¼
E½ðX 2m
X
ÞðY 2m
Y
Þ?
s
X
s
Y
where X and Y are two variables of which the relationship is to be evaluated, m
X
and m
Y
are expected values for X and Y, respectively, s
X
and s
Y
are their standard deviations, and
E is the expected value operator.
8. For stock markets it is important to consider whether they should be measured in dollars or
the domestic currency for the speci?c question being investigated. For example, for portfolio
allocations by US investors the dollar value is most relevant while for looking
at the sensitivity of a foreign market to a US shock the domestic currency value is more
relevant.
9. For an application to the measurement of business cycle correlations and their implications
for OCA analysis, see Willett et al. (2010).
10. For discussion of how the HP ?lters are estimated, see Appendix A of the longer version of
this paper that appears on the web site of the Claremont Institute for Economic Policy
Studies: www.cgu.edu/pages/1380.asp. For evaluation of the HP ?lter, see Ravn and Uhlig
(2002).
11. See Appendix B in the longer version of this paper.
12. For more evaluation of DCC-GARCH, see Engle and Kevin (2001) and Vargas (2006).
Measuring
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13. The frequency approach is based on time varying coherence to detect endogenously
structural changes in the comovement process. This method not only detects comovement
dynamics in different cycles, but also tests if these countries tend to be more synchronized
or not. The coherence is interpreted as the squared linear correlation coef?cient for each
frequency of the spectra of two series. When calculating the time varying coherence,
they employ the Bai and Perron (1998) test to determine endogenously break dates because
the choice of this type of model is motivated by TVCF characteristics.
14. For more econometric details of the TVCF analysis, see Essaadi and Boutahar (2008).
15. The short-run effects are measured by a vector-correction model and the long-run effects
are measured by conintegration tests, for example, Johansen (1991) procedure.
16. While standard OLS assumes a simple linear relationship among the variables,
quartile analysis allows the estimation of different coef?cients for different parts of the
distributions.
17. The reduced form presentation of the VAR model is as the following:
Yt ¼ c þ A1Yt 21 þ A2Yt 22 þ · · · þ ApYt 2p þ et
where Yt is a set of k time series variables: Yt ¼ (Y1t, Y2t. . .Ykt)
0
, the Ai s are k £ k
matrices of coef?cients, c is a k £ 1 vector of constants, p is the order or lag of the model, and
et is a k £ 1 vector of error terms – the et s are serially uncorrelated but may
be contemporaneously correlated.
18. GMM is a generic method to estimate parameters when the full shape of the distribution
function of the data is not known and the parameters of interest are ?nite-dimensional.
19. The restrictions de?ne the endogenous relationship among variables and can be realized by
multiplying a control matrix determining the order of the effects.
20. More applications of factor analysis can be found in studies by Otrok and Whiteman (1998),
and Bernanke et al. (2005).
21. The pioneering use of this approach to attempt to distinguish real versus nominal shocks is
by Blanchard and Quah (1989).
22. For technical details see Appendix C of the longer version of this paper.
23. Li also ?nds that regional factors and country factors also play a more important role in
explaining gross import ?uctuations than in explaining gross exports (Li, 2011).
24. Of course they also often ?nd similar results. For example, both Li (2011) and Zhang (2011a)
?nd that the use of linear versus HP trends makes little difference in their studies of
macroeconomic and stock market interdependencies.
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About the authors
Linyue Li (PhD, Claremont Graduate University) serves as the Research Associate at the
Claremont Institute for Economic Policy Studies.
Nan Zhang (PhD, Claremont Graduate University) serves as the Research Associate at the
Claremont Institute for Economic Policy Studies and is also a Senior Research Analyst at the
Milken Institute.
Thomas D. Willett (PhD, University of Virginia) serves as Director of the Institute for
Economic Policy Studies and is also Horton Professor of Economics at Claremot Graduate
University and Claremont McKenna College. Thomas D. Willett is the corresponding author and
can be contacted at: [email protected]
Measuring
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145
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doc_310763069.pdf
Study of the interdependence among economies is of considerable importance. This area
includes issues such as the increasing importance of regional economic interactions, the effects of
economic growth and recession in the advanced economies on emerging market countries, and
financial contagion. A wide range of related terms and methodologies are used in the literature of
interdependence. The purpose of this paper is to review the major concepts and various measurements
of interdependence in financial markets and the real economy, serving as a reference and benchmark
for future research on interdependence among specific regional or global economies
Journal of Financial Economic Policy
Measuring macroeconomic and financial market interdependence: a critical survey
Linyue Li Nan Zhang Thomas D. Willett
Article information:
To cite this document:
Linyue Li Nan Zhang Thomas D. Willett, (2012),"Measuring macroeconomic and financial market
interdependence: a critical survey", J ournal of Financial Economic Policy, Vol. 4 Iss 2 pp. 128 - 145
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Measuring macroeconomic and
?nancial market
interdependence: a critical survey
Linyue Li
Claremont Institute for Economic Policy Studies, Claremont, California, USA
Nan Zhang
Claremont Institute for Economic Policy Studies, Claremont, California,
USA and
Milken Institute, Santa Monica, California, USA, and
Thomas D. Willett
Claremont Institute for Economic Policy Studies, Claremont, California, USA
Claremont Graduate University, Claremont, California, USA and
Claremont McKenna College, Claremont, California, USA
Abstract
Purpose – Study of the interdependence among economies is of considerable importance. This area
includes issues such as the increasing importance of regional economic interactions, the effects of
economic growth and recession in the advanced economies on emerging market countries, and
?nancial contagion. A wide range of related terms and methodologies are used in the literature of
interdependence. The purpose of this paper is to review the major concepts and various measurements
of interdependence in ?nancial markets and the real economy, serving as a reference and benchmark
for future research on interdependence among speci?c regional or global economies.
Design/methodology/approach – Major measurements of interdependence are reviewed from
simple approach to more complicated ones, and strengths and weaknesses of the various
measurements of interdependence are discussed.
Findings – This paper surveys the various major measurements of interdependence and illustrates
how they have been used to address a substantial range of issues.
Originality/value – The paper shows that studies of macroeconomic and ?nancial interdependence
use the same types of econometric measurements. The review and critiques of these various types of
measures should be of value to those wishing to do research in these areas and also to those wishing to
have a better understanding of papers that they read.
Keywords Macroeconomics, Financial markets, Econometrics, Interdependence, Contagion,
Financial crisis, Econometric measurements
Paper type Research paper
1. Introduction
The rapid increase in international trade and capital ?ows associated with
globalization has generated substantial interest in issues of ?nancial and economic
interdependence. These are often discussed under the labels of the international
transmission mechanisms, business cycles and stock market synchronization,
decoupling and recoupling, and international contagion.
The global ?nancial crisis drew even more attention to the subject as the impacts of US
subprime crisis on the world economies have providedevidences of global interdependence.
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1757-6385.htm
JFEP
4,2
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Journal of Financial Economic Policy
Vol. 4 No. 2, 2012
pp. 128-145
qEmerald Group Publishing Limited
1757-6385
DOI 10.1108/17576381211228989
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Following the US subprime crisis, the ?nancial markets and real economies in many
advanced and emerging market countries were hard hit. Many ?nancial institutions
sufferedhuge losses due to their massive exposure to the subprime loans in2008 andthe US
stock market collapsed. The Dow Jones Industrial Average dropped 18.1 percent and the
S&P 500 fell more than 20 percent within one week in October after Lehman brothers ?led
for bankruptcy. The turmoil in the US ?nancial market triggered the global crisis. Many
international stockmarkets experiencedtheir worst short termdeclines intheir history with
drops of around 10 percent in many indices within one day in October 2008.
Initially it was believed that the adverse effects on economic growth would be
largely limited to the USA and Europe whose banks were most affected, but as these
economies fell into recessions, their large drops in imports began to hit heavily much of
the rest of the world (for discussion of the slow spread of the crisis, see Willett et al.
(2010)). While US output growth fell to zero in 2008 and to 22.6 percent in 2009, world
output growth in 2009 also turned negative in 2009 (International Monetary Fund,
2010). Many had argued that with their rapid growth and the increase in intra regional
interdependence, many of the emerging market economies (EMEs) had largely
decoupled from the advanced economies. The crisis showed that this was overstated
and highlighted the importance of careful empirical studies of global economic
interdependence.
The study of ?nancial and economic interdependence among countries and regions
includes research not only under this speci?c title, but also under related terms such as
business cycle synchronization, recoupling/decoupling, the extent of ?nancial and
economic integration, contagion, and so on. Most studies concentrate on a limited number
of aspects of interdependence. Many of these aspects are closely related, however, and
make use of the same types of measurement techniques such as correlation analysis,
vector auto-regression (VAR) analysis (impulse response functions), and factor analysis.
This paper focuses on the interrelationships among these different sets of studies,
outlines the various major types of measurement techniques used and discusses their
strengths and weaknesses. These are illustrated with reviews of a number of recent
studies of major types of issues such as business cycle synchronization, and linkages
such as contagion.
2. Channels and concepts of interdependence
Economic interdependence is a broad term covering the whole range of ways in which
the behavior of variables such as stock market movement, interest rate, economic
growth, etc. is in?uenced by developments in other economics. We can think of greater
economic integration or globalization through international trade and capital ?ows as
leading to greater international transmission of developments in one country to other
countries and thus generating greater economic interdependence.
If one country goes into recession, it will decrease its imports from other countries.
Its trading partners thus face reduction in their exports which in turn reduces their
economic growth. The large reduction in the exports of developing countries caused by
the recession in the advanced economies that resulted from their recent ?nancial crises
is an important example. Obviously, the greater are trade ?ows among countries, the
greater those effects will be.
Likewise, international capital ?owlink ?nancial and economic developments in one
countrytothose inother countries. For example, bothlower interest rates inthe advanced
Measuring
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economies and increased economic growth in EMEs increase the incentives for larger
capital ?ows to the latter. This in turn may make these countries vulnerable to a sudden
stop of these ?nancial ?ows as we have seen in a number of crises.
Recent research has found that not only the levels but also the composition of trade
and capital ?ows can have a large effect on the international transmission of
such developments. For example, short term bank loans and portfolio investment are
much more susceptible to contagion effects than are ?ows of direct investment[1].
Likewise, empirical studies have found that intra-industry trade plays a major role in
increasing business cycle transmission, while inter-industry trade tends to reduce
macroeconomic interdependence due to sector speci?c shocks (for analysis and
references, see Li (2011)).
Below we brie?y discuss the topics of business synchronization among
economies, the debate over decoupling and recoupling, and the spread of ?nancial
contagion.
2.1 Business cycle synchronization
In the classic de?nition by Burns and Mitchell (1946), business cycle synchronization
occurs when:
[. . .] a cycle consists of expansions occurring at about the same time in many economic
activities, followed by similarly general recessions, contractions, and revivals which merge
into the expansion phase of the next cycle.
According to their de?nition:
[. . .] this sequence of changes is recurrent but not periodic; in duration business cycles vary
from more than one year to ten or twelve years; they are not divisible into shorter cycles of
similar character with amplitudes approximating their own.
Business cycles are usually measured by GDP growth rate, domestic consumption
growth rate, domestic investment growth rate, employment rate, and in?ation. Studies
focus on both the strength of the relationships and the factors that in?uence them. One
major topic is the effect of trade ties on synchronization measures. These relationships
are important for issues of macroeconomic policy coordination and are one of the
major criteria developed in the literature on optimum currency areas for determining
the costs and bene?ts of joining a common currency area[2].
2.2 Recoupling/decoupling
In recent years there has been considerable debate about the extent to which emerging
market countries are decoupling or recoupling with the advanced economies. While
increasing globalization would be expected to generate stronger coupling among
economies, some have argued that the more rapid growth in EMEs and increases in
regionalization would lead to a decoupling of their economic growth and stock market
performance from developments in the advanced economies[3]. In this view, while
economic interdependence has been increasing among some sets of countries, it has
been decreasing among others. As decoupling implies a break in a relationship that
was previously closely linked, this de?nition lends itself naturally to discussions of
changes in patterns of comovements or correlations.
JFEP
4,2
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2.3 Contagion (or spillover)
Contagion is usually referring to a spread of a crisis from one economy to another. The
term of contagion is analogous to the spread of contagious diseases and became
particularly popular since the Mexico peso crisis in the end of 1994.
Contagion implies the existence of interdependency but there can be a number of
different types of interdependency and these can have very different implications for
policy[4]. As a result, the term contagion requires one to specify what type in order to
communicate effectively. In public discussions, there is often a connotation that
contagion is due to irrational panic and thus re?ects a failure of markets to operate
ef?ciently. This is far from the only possible type of contagion, however. In its broadest
sense, contagion can refer to any spread of economic developments in one country to
other countries. In this sense, it is the same as interdependence. More commonly,
contagion is used to refer to stronger than normal interrelationships associated with
crises. One popular de?nition of contagion is that a crisis in one country increases the
risk of crises inother economies. This type is measuredbyestimatingthe extent to which
a crisis in one country increases the probability of crises in others from equations that
control for a number of factors such as current account de?cits, low international
reserves and rapid credit growth that are often associated with crises[5].
For looking at effects on variables such as interest rates and stock prices, continuous
measures are used. A typical de?nition of this more restricted view of contagion is the
transmission of shocks to other countries or the cross-country correlation beyond any
fundamental link among the countries and beyond common shocks (which is also called
the “pure contagion”). This de?nition is usually referred to as excess comovements,
commonly explained by herding behavior. This is often measured by looking at the
increase in cross-country correlations increase during “crisis times” relative to
correlations during “tranquil times”.
Interpretations of the causes of the particular crises vary widely. In large part this is
because there are often multiple causes and these can vary from one case to another.
Thus, careful empirical and case studies are essential to shed light on such episodes.
3. Measurements of interdependence
The literature provides many methods to measure various aspects of interdependence.
Here we discuss a number of the most widely used measures: correlation, cointegration,
panel, VAR, and dynamic factor analyses[6]. Some research uses the combination of
more than one of the above methods.
Table I summarizes the main measurements of interdependence discussed in this
paper. These are discussed beginning with the simplest approach, correlation analysis,
and moving on to more complicated ones and from single variable to multiple variable
analyses.
3.1 Correlation or comovement analyses
Correlation or comovement analysis is one of the most widely used measurements in
recent interdependence or contagion literature. It includes static and dynamic analysis.
It is important to remember that correlation need not imply causation, nor does it
measure only the degree of interdependent among variables. Correlations are
often in?uenced by common shocks. Furthermore, even with country speci?c shocks
correlations across countries will often vary with the nature of the shock.
Measuring
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a
t
e
r
r
o
r
s
a
r
e
n
o
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m
a
l
l
y
d
i
s
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r
i
b
u
t
e
d
t
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o
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a
y
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r
e
a
t
l
y
a
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f
e
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t
e
d
b
y
o
u
t
l
i
e
r
s
E
x
a
m
p
l
e
:
E
n
g
l
e
(
2
0
0
2
)
,
I
n
t
e
r
n
a
t
i
o
n
a
l
M
o
n
e
t
a
r
y
F
u
n
d
(
2
0
0
8
)
T
V
C
F
T
h
e
T
V
C
F
n
o
t
o
n
l
y
c
a
p
t
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r
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d
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t
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b
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t
a
l
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r
b
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a
v
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i
n
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a
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f
r
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q
u
e
n
c
y
.
T
h
e
c
o
h
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r
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n
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e
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r
p
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r
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a
t
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i
e
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f
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a
c
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f
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u
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y
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e
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t
r
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t
w
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r
i
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E
x
a
m
p
l
e
:
E
s
s
a
a
d
i
a
n
d
B
o
u
t
a
h
a
r
(
2
0
0
8
)
A
d
v
a
n
t
a
g
e
:
i
t
n
o
t
o
n
l
y
d
e
t
e
c
t
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c
o
m
o
v
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m
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n
t
d
y
n
a
m
i
c
s
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n
d
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f
f
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r
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n
t
c
y
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l
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b
u
t
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l
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h
a
n
g
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n
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y
n
c
h
r
o
n
i
z
a
t
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o
n
p
r
o
c
e
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s
e
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a
t
d
i
f
f
e
r
e
n
t
f
r
e
q
u
e
n
c
i
e
s
;
t
h
e
f
r
e
q
u
e
n
c
y
a
p
p
r
o
a
c
h
d
o
e
s
n
o
t
d
e
p
e
n
d
o
n
a
n
y
d
e
t
r
e
n
d
i
n
g
t
e
c
h
n
i
q
u
e
a
n
d
d
o
e
s
n
o
t
h
a
v
e
t
h
e
“
e
n
d
-
p
o
i
n
t
”
p
r
o
b
l
e
m
s
(
c
o
n
t
i
n
u
e
d
)
Table I.
Summary of
measurements of
interdependence
JFEP
4,2
132
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
M
e
t
h
o
d
o
l
o
g
y
S
u
b
-
c
a
t
e
g
o
r
y
D
e
s
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r
i
p
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i
o
n
A
d
v
a
n
t
a
g
e
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n
d
d
i
s
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d
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o
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n
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r
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t
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a
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l
y
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e
s
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o
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r
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a
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r
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.
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a
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a
t
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i
f
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r
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E
x
a
m
p
l
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B
u
r
d
e
k
i
n
a
n
d
S
i
k
l
o
s
(
2
0
1
1
)
A
d
v
a
n
t
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g
e
(
a
l
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o
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d
v
a
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)
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t
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n
w
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d
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T
h
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p
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o
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b
l
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e
d
a
s
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f
t
h
o
s
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u
a
n
t
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t
i
e
s
w
e
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e
n
o
n
-
r
a
n
d
o
m
E
x
a
m
p
l
e
:
B
a
u
r
a
n
d
F
r
y
(
2
0
0
8
)
,
I
n
t
e
r
n
a
t
i
o
n
a
l
M
o
n
e
t
a
r
y
F
u
n
d
(
2
0
0
8
)
A
d
v
a
n
t
a
g
e
:
t
h
e
p
a
n
e
l
m
o
d
e
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n
a
b
l
e
s
g
r
e
a
t
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x
i
b
i
l
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t
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n
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d
e
l
i
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d
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f
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a
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t
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f
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n
d
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d
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a
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f
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t
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D
i
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d
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a
n
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:
t
h
e
p
a
n
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l
m
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d
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r
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l
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s
o
n
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A
R
a
n
a
l
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T
h
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V
A
R
m
o
d
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a
n
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a
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a
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f
t
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p
a
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t
e
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o
l
u
t
i
o
n
A
d
v
a
n
t
a
g
e
:
i
t
c
a
n
a
n
a
l
y
z
e
t
h
e
e
f
f
e
c
t
o
f
t
h
e
i
n
n
o
v
a
t
i
o
n
a
l
s
h
o
c
k
s
a
l
l
o
w
i
n
g
i
n
t
e
r
a
c
t
i
o
n
s
a
m
o
n
g
v
a
r
i
a
b
l
e
s
a
n
d
p
r
o
v
i
d
e
s
d
y
n
a
m
i
c
s
o
l
u
t
i
o
n
s
w
h
i
c
h
a
r
e
o
f
t
e
n
h
i
d
d
e
n
t
o
s
t
a
n
d
a
r
d
p
r
o
c
e
d
u
r
e
s
s
u
c
h
a
s
O
L
S
o
r
o
t
h
e
r
s
t
a
t
i
c
r
e
g
r
e
s
s
i
o
n
s
E
x
a
m
p
l
e
:
K
i
m
e
t
a
l
.
(
2
0
0
9
)
D
i
s
a
d
v
a
n
t
a
g
e
:
t
h
e
r
o
b
u
s
t
n
e
s
s
o
f
t
h
e
V
A
R
e
s
t
i
m
a
t
i
o
n
s
d
e
p
e
n
d
s
o
n
a
p
l
a
u
s
i
b
l
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s
e
t
u
p
o
n
t
h
e
e
n
d
o
g
e
n
o
u
s
a
s
s
u
m
p
t
i
o
n
s
a
m
o
n
g
v
a
r
i
a
b
l
e
s
;
a
l
l
e
f
f
e
c
t
s
o
f
o
m
i
t
t
e
d
v
a
r
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a
b
l
e
s
a
r
e
i
n
t
h
e
r
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s
i
d
u
a
l
s
,
w
h
i
c
h
m
a
y
l
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a
d
t
o
m
a
j
o
r
d
i
s
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o
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t
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o
n
s
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n
t
h
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p
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s
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r
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s
p
o
n
s
e
s
,
m
a
k
i
n
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t
h
e
m
m
o
r
e
d
i
f
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c
u
l
t
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r
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r
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y
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y
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t
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p
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a
n
d
r
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l
a
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p
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t
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r
i
e
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a
n
d
e
x
p
l
a
n
a
t
o
r
y
v
a
r
i
a
b
l
e
s
E
x
a
m
p
l
e
:
K
o
s
e
e
t
a
l
.
(
2
0
0
8
a
,
b
)
A
d
v
a
n
t
a
g
e
:
i
t
a
l
l
o
w
s
f
o
r
t
h
e
s
e
p
a
r
a
t
i
o
n
o
f
i
d
i
o
s
y
n
c
r
a
t
i
c
c
o
m
p
o
n
e
n
t
s
a
n
d
c
o
m
m
o
n
c
o
m
o
v
e
m
e
n
t
s
.
T
h
e
r
e
f
o
r
e
,
t
h
e
d
y
n
a
m
i
c
f
a
c
t
o
r
s
c
a
n
d
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s
c
r
i
b
e
c
o
n
t
e
m
p
o
r
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o
u
s
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n
d
t
e
m
p
o
r
a
l
c
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r
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a
n
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a
m
o
n
g
t
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e
v
a
r
i
a
b
l
e
s
D
i
s
a
d
v
a
n
t
a
g
e
:
i
t
n
e
e
d
s
r
e
l
a
t
i
v
e
l
y
l
o
n
g
t
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m
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r
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s
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n
d
i
t
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a
s
y
t
o
l
o
s
e
d
e
g
r
e
e
s
o
f
f
r
e
e
d
o
m
.
I
n
a
d
d
i
t
i
o
n
,
i
t
c
a
n
n
o
t
b
e
u
s
e
d
t
o
a
n
a
l
y
z
e
b
i
l
a
t
e
r
a
l
c
o
m
o
v
e
m
e
n
t
s
b
e
t
w
e
e
n
c
o
n
c
e
r
n
e
d
c
o
u
n
t
r
i
e
s
Table I.
Measuring
interdependence
133
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
As a result the correlations among asset prices used for the allocation of investment
portfolios and among economic growth rates used to look for patterns and trends in
macroeconomic interdependence often vary considerably over time.
3.1.1 Static correlations.
(a) Simple correlation. The simple correlation measures the overall comovements
and serves as the basic framework for a quick assessment of interdependence. It is
obtained by dividing the covariance of the two variables by the product of their
standard deviations[7].
Many researchers have used correlation analysis to test for interdependency or its
related terms. For example, de?ning contagion as a signi?cant rise in the correlation
among asset returns, Baig and Goldfajn (2001) test for evidence of contagion between
the ?nancial markets of Asian countries during the crisis of 1997-1998 and ?nd that
correlations for currency and sovereign spreads increase signi?cantly while the equity
market correlations offer mixed results.
The advantage of the simple correlation is its straightforwardness and intuitiveness.
However, it has important limitations. One of the most important limitation is that as
noted above correlations are the product of both the sensitivity of developments in one
country to those in another (interdependence) and those developments themselves.
Thus, a high correlation could occur because of a common shock. This explains a
substantial portion of the increases in the correlation of growth rates among Asian
economies during their ?nancial crisis of 1997-1998 and among many of the advanced
economies during the ?nancial crisis of 2007-2009 (Willett et al., 2010; Zhang, 2011a).
Different patterns of shocks within the economies in question can also lead to
substantial differences incorrelations. Thus, we often see correlations varysubstantially
over time. Zhang (2011a) ?nds this for stock returns of the USA and Asian economies.
Correlations almost always rise during crises. The frequent instability of correlations
suggests that we should be careful not to misinterpret short run variations as the
beginning of long run trends.
One controversial aspect of using changes in correlations to measure contagion
concerns how to test for statistical signi?cance. Forbes and Rigobon (2001, 2002), for
example, criticize that the simple correlation is biased in the analysis of contagion
because of the presence of higher volatility in market returns in the crisis periods leads
to increased correlations. Correcting for the heteroskedasticity problem, they ?nd
virtually no evidence of contagion during the multiple crises and that the greater
degree of comovement of the stock market during the crisis period may simply re?ects
a continuation of the trend in market interdependence. However, other researchers
argue that the increase in variance is a normal part of a crisis and so adjusting for these
when testing signi?cance may not be appropriate (Baig and Goldfajn, 2001).
One important issue that studies often do not address suf?ciently is the length of the
time periods over which correlations should be measured. Appropriate lengths can
vary substantially depending on the speci?c issue being addressed. For example,
managers of investment funds attempting to beat the market may be interested in
correlations over very short time periods, while considerably longer time periods
would be relevant for issues of macroeconomic policy coordination[8].
A related issue with simple correlations is that they do not distinguish long run
relationships, i.e. trends, from short run movements around these trends. In general
macroeconomic interdependence will have a larger impact on these shorter term
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movements than on the trends themselves, although of course the extent to which an
economy is opened to the world economy can affect its growth rate[9]. An obvious way
of dealing with this problem is to look at the correlations of deviations from trends.
(b) Trend-?ltered correlations (linear and non-linear trend). The trend-?ltered
correlations remove the effects of medium or long term trends (linear or non-linear) and
?nd the comovements on detrended data. Generally speaking, there are two categories
of de-trending methods, linear and non-linear. For linear de-trending techniques,
ordinary least squares (OLS) regression is often used to estimate a linear trend-line. For
non-linear de-trending techniques, the Hodrick-Prescott (HP) ?lter is often selected.
Willett et al. (2011), for example, calculate both the linear detrended and HP ?lter
detrended correlations between the growth rate of US and several EMs. Because
economies such as China and India continue to have high growth rates, it is often
conclude that they are little affected by the global ?nancial crisis. When one adjusts for
their high trend growth rates, however, it is found that they suffer declines on the same
order as the advanced economies. Thus, while simple analysis supports the decoupling
hypothesis, more careful analysis ?nds the supports the opposite conclusion.
The HP ?lter smoothes data with a procedure of squared error minimization and then
removes short-term ?uctuations[10]. A main drawback of the HP ?lter is the end-point
problem. The calculation puts more weight on the observations at the end of the series
(Marinheiro, 2005). But if the studyhas relatively large numbers of observations and focus
on mostly the middle points, the biases are limited. Although the simple correlations and
the static trend-?ltered correlations are straightforwardandeasyto calculate, theyare less
convenient to capture high frequent time varying or dynamic characteristics of
the comovements which are often shown in the fast-changing ?nancial markets. The
dynamic correlation methods can solve this problem by providing dynamic solutions.
3.1.2 Dynamic correlations. Dynamic correlations provide time-varying correlations
between economic variables. Some examples are the dynamic conditional correlations
(DCC)-GARCH-developed by Engle (2002) and the time varying coherence functions
(TVCF) used by Essaadi and Boutahar (2008).
(a) Dynamic conditional correlations-GARCH. DCC-GARCH developed by Engle
(2002) takes the volatility or heteroscedasticity, and autocorrelation of the variables
into account to produce a time-varying calculation of correlations. It is estimated in a
two-stage procedure. First, univariate GARCH models are ?tted for each of the
variables in the speci?cation. Then using transformed residuals resulting from the ?rst
stage, the DCC estimators are estimated[11].
This method has then been widely used in the research on contagion. For example,
Wang and Thi (2006) use it to examine the impact of the 1997-1998 Asian ?nancial
crises on the Chinese economic area and ?nd positive correlation coef?cients of stock
returns. The International Monetary Fund (2008) in its Global Financial Stability
Reports uses DCC-GARCH to analyze the comovements in stock markets between the
USA and some global emerging market regions as a whole and ?nd varied but overall
increasing correlation levels during the past several years up to 2008. A study by
Zhang (2011a) calculates DCC for the stock returns of some Asian economies and the
USA during the recent ?nancial crisis and ?nds that the correlations of Asian equity
markets with the USA have tended to increase over time, but that there was a
decrease in correlations during the beginning of the recent crisis ( July 2007-August
2008), then a substantial increase after the collapse of Lehman brothers up to late 2009
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(September 2008-August 2009), and a small decrease in the later period of the recent
crisis (September 2009-August 2010). Much of the decoupling debate wrongly focused
on these short run changes as if they re?ected changes in longer term trends.
DCC-GARCH method has proven to be more robust than the static correlation
models, especially for looking at ?nancial variables which often face greatly changing
volatility. DCC-GARCH has limitations, however. For example, the parameters of the
DCC-GARCH model assume that errors are normally distributed may be greatly
affected by outliers. While the normality assumption ?ts the actual behavior of
?nancial markets fairly well during calm periods, it breaks down badly during crisis
periods which display much larger changes than predicted by the normal distribution.
This is a major problem for many of the risk models used by the ?nancial sector[12].
(b) Time varying coherence functions. The time varying coherence analysis is
another example of dynamic methods of comovement analysis. Essaadi and Boutahar
(2008), use this approach[13] to estimate TVCF for non-stationary time series to capture
both degrees of comovements and their behavior in each frequency[14]. Their research
?nds that there is a common business cycle in East Asia, especially after the 1997-1998
crises.
The main advantage of the frequency approach TVCF is that it not only detects
comovement dynamics in different cycles but also identi?es changes in synchronization
processes at different frequencies. In addition, the frequency approach does not depend
on any particular detrending technique and does not have the “end-point” problems.
3.2 Cointegration analyses
Cointegration tests capture one aspect of the integration relationships among
economies. Mathematically, if some linear combination of two or more series such as
in?ation in two countries has a lower order of integration, the series are considered
cointegrated. Let us explain.
Empirical macroeconomic studies frequently involve variables with trends such as
the money supply, price level, and aggregate economic growth and some of its
components such as consumption and investment. Such series are often non-stationary.
Regression of one of such variables on another would be misleading since much of the
correlation would be due to common trends. Thus, simple regression relationships could
be spurious. To manipulate these series appropriately, the procedure of taking ?rst
differences I(1), or second differences I(2), or other transformations (such as seasonal
adjustment) is used to reduce them to stationarity (Greene, 2008). Thus, for example,
while the price level has a strong trend, its ?rst differ, the in?ation rate, may not.
In?ation may also have a strong trend than it would be differenced again.
With theory testing, an important issue with such differencing is whether the
predicted relationships would still hold up in difference form. Thus, while we would
expect money growth and in?ation to be correlated, the expected relationship between
money growth and the ?rst difference of in?ation is unclear relationships.
Generally speaking, if two time series are integrated to different orders, linear
combinations of themwill be integrated to the higher of the two orders. If both series are
each drifting upward with their own trend, then the difference between themshould also
be growing, with yet another trend, unless there is some relationship between those
trends. For example, if the two series are both I(1), then there may be a vector of
parameters such that the disturbances are I(0) (i.e. a stationary, white noise series).
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Intuitively, this phenomenon would imply that the two series are drifting together at
roughlythe same rate. If the two series satisfy this requirement, they are considered to be
cointegrated (Greene, 2008).
In this case, we can distinguish the long-run relationship between the two series, that
is, the manner in which the two variables move upward together, and the short-run
dynamics, that is the relationship between deviations of each series from its long-run
trend. If there exist cointegration relationship between two time series, transforming
them to stationary data through 1st difference or 2nd difference procedure would hide
the long-run relationship between the two time series. Thus, the cointegration test is
usually used to analyze the long-run relationship between the economic variables when
all the variables are found to be non-stationary (i.e. there exist unit roots).
Error correction estimation is often used to investigate the short-run interactions of
the economic series such as growth rates or stock markets. As an example, Karolyi and
Stulz (1996) break down the comovement of stock markets between the USA and Japan
into long-run and short-run linkages[15].
Cointegration tests are often used both in studies of ?nancial and macroeconomic
integration and contagion. Burdekin and Siklos (2011), for example, examine long-run
cointegrating relationships for the Asian economies and the USA during 1999-2010 and
apply quantile regression techniques[16] to allow for variation over the spectrum of the
return distributions. They ?nd that the enormous growth of the Shanghai market in
the new millennium has been accompanied by substantial integration with other
regional and world market. The major advantage as well as limitation of the
cointegration method is that it is only suitable to analyze the long-run relationship
between non-stationary economic variables.
3.3 Panel analyses
Panel data sets combine time series and cross section data. Thus, one is able to
investigate relationships both across countries and markets and over time (Greene,
2008). A variety of different models for panel data can be constructed. Broadly, they
fall into the following three categories:
(1) Pooled regression. If individual effect contains only a constant term, the OLS
provides consistent and ef?cient estimates of the parameters.
(2) Panel regression with ?xed effects. Individual effects can be modeled as
coef?cients on individual-speci?c binary variables. Most economists favor
using “?xed effects because this form of unobserved heterogeneity can be either
correlated with regressors or uncorrelated with them, just as any other
regressors can be” (Greene, 2008).
(3) Panel regression with random effects. Some models make the strong assumption
that individual effects are “random” in the sense that they must be uncorrelated
with all regressors. In this form of setting, unobserved heterogeneity affects the
residuals in the equation of interest.
The major difference between ?xed and random effects is whether the unobserved
individual effect embodies elements are correlated with the regressors in the model, not
whether these effects are stochastic or not. The ?xed effects estimator of the slope
parameters is consistent even if the true model is pooled or with random effects,
because the ?xed effects model allows individual effects to be correlated with other
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explanatory variables but it does not require the variables to be correlated. The
random effects model is not consistent if the true model is ?xed effects model, because
the random effects model imposes “no correlations between the individual effect and
the other explanatory variables” (Greene, 2008). In addition, the random effects model
allows the identi?cation of the marginal effects of time-invariant regressors to avoid
“perfect multicollinearity” when the ?xed effects model is applied, because the
individual effect is just a shock in the random effect model, but the estimates are
consistent only if the strong assumptions underlying the random effects are valid
(i.e. no correlations between the individual effect and the other explanatory variables).
Yeyati (2011) provides an example of the use of pooled panel regression to
investigate the decoupling of the main emerging markets from the advanced economies
in both ?nancial and real terms. The panel regressions for the annual growth rate of
country’s cyclical output (relative to a log linear GDP trend) on the G7 and Chinese
cycles for the periods of 1993-1999 and 2000-Q3 2010 show that the growth of some
emerging markets are becoming less coupled with the advanced economies and more
coupled with China.
Baur and Fry (2008) apply a panel ?xed time effects model to equity returns for
11 Asian economies during the Asian crisis of 1997-1998 and ?nd that interdependencies
are substantially more important than contagion during the crisis. The ?xed time effects
are interpreted in comparison to a base period and are assumed to re?ect contagion.
Actually, the ?xed time effects can capture movements across all asset markets that are
not explained by regional or global factors. Baur and Fry address that system-wide
contagion exists if the value of the ?xed time effect is greater than a threshold based on
conventional (99 percent) signi?cance levels.
Using high signi?cance requirements is certainly correct for scienti?c analysis
where we should require strong evidence to accept a hypothesis. From a policy
perspective, however, such stringent requirements are questionable. They implicitly
assume no contagion as the base presumption. Signi?cance from the standard types of
tests at say the 30 percent level would still suggest that the odds were 70-30 that there
was some contagion and policy makers would not have the luxury of waiting for a
larger number of observations before making decisions.
The International Monetary Fund (2008) in its Financial Stability Report investigates
the spill-over effects in equity markets fromthe advanced economies to EMEs fromMay
2008 to January 2011 using a ?xed effect panel analysis. The estimation on Asian equity
markets, for example, suggests that the global factors are statistically signi?cant: equity
prices are positively associated with global excess liquidity and negatively with credit
and market risk prime. For domestic factors, GDP growth, an expected exchange rate
appreciation, and an increase of market capitalization have positive effects on equity
prices, while interest rate differentials have a negative effect.
The fundamental advantage of panel analysis over a cross section is that
researchers have great ?exibility in modeling differences in behavior across individual
units and their dynamics. Of course panel analysis requires a larger set of variables
than correlation or cointergration tests.
3.4 Vector auto-regression (VAR) analyses
VAR analysis takes endogeneity of different economic variables into account when
investigating interdependencies among economies. It analyzes the dynamic impact
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of random disturbances and describes the evaluation of a set of endogenous variables
in the system as a linear function of their past evolution[17]. VAR models are usually
presented with impulse response functions that measure the effects of the different
shocks in one variable on the other variables, and variance decompositions that
measure the relative importance of the different shocks to the variation in the different
variables. Granger causality tests are often used in the VAR analysis to decide the
endogeneity of the variables. These disclose statistical but not necessarily behavioral
causality.
Kimet al. (2009), for example, investigate the degree of real economic interdependence
between nine emerging Asian countries and major industrial countries including Japan
and the USA. They document the evolution of macroeconomic interdependence for the
emerging Asian economies through changing trade and ?nancial linkages at both
the regional level and the global level. They apply a panel VAR model to estimate the
degree of real economic interdependence measured by aggregate output growth rates
before and after the 1997-1998 Asian crises. Their empirical ?ndings show that real
economic interdependence increased signi?cantly in the post-crisis period, indicating
“recoupling”, rather than decoupling.
The International Monetary Fund (2007) in its WEO report also uses the VAR
method to examine the spillover of the USA to other 130 economies in GDP and ?nds
that in general, the spillovers from growth in the USA are signi?cantly higher in the
post-1987 half of the sample (1970-2005). This suggests that perceived large declines in
the macroeconomic importance of the USA have been overstated and that the rapidly
increasing trade intergration in many regions has not undercut the importance of
global interdependence.
Using a structural VAR in the generalized method of moments (GMM) model[18],
Angkinand et al. (2010) ?nd an increase in interdependence between advanced country
stock market returns over time and that the spillover effects from the USA to other
industrial countries are particularly large during the recent ?nancial crisis. A study by
Zhang (2011b) investigates the impact of US stock market movements on Asian
markets during the recent ?nancial crisis using VAR analysis and ?nds that global
factors, especially the US equity market, also effect Asian equity markets more
strongly during the crisis.
The major advantage of the VAR method is that it analyzes the effects of shocks
allowing for interactions among variables and provides dynamic estimates. The VAR
method provides a systematic approach to imposing restrictions[19] and to de?ne
endogeneity among variables and capture relationships which are often hidden to
standard procedures such as OLS regressions. A limitation of the method is that the
robustness of the VAR estimations depends on a plausible setup on the endogenous
assumptions among variables. Another limitation of the VAR approach is that as it has
to be estimated with limited number of variables, all effects of omitted variables will be
in the residuals. This may lead to major distortions in the impulse responses, making
structural interpretations more dif?cult.
3.5 Dynamic factor analyses
Dynamic factor analysis is a technique used to detect common patterns in a set of time
series and relationships between these series and explanatory variables[20]. Taking
the model used by Kose et al. (2008a, b) as an example, dynamic factor analysis
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characterizes the degree of synchronization over time in various dimensions (global
factor, regional factors, country factors, and idiosyncratic factors) without making
strong identifying assumptions to disentangle different types of common shocks[21].
The dynamic relationships in the model are captured by modeling each factor and
idiosyncratic component as an autoregressive process. This simultaneously picks up in
a ?exible manner, the contemporaneous spillovers of shocks as well as the dynamic
propagation of business cycles without putting a priori restrictions on the structure of
the propagation mechanism or the directions of spillovers[22]. A surprising conclusion
of their analysis is that contrary to what would be expected from increased
globalization, the global factor was less important in the second period.
Li (2011) adds additional macroeconomic variables such as exports and imports to
Kose et al.’s model and also ?nds that the world factor has become less important in
explaining the macroeconomic ?uctuations from sub-period 1961-1984 to sub-period
1985-2007. Li also ?nds that contrary to perceptions of increased regionalization,
regional factors do not play an important role in explaining aggregate volatility except
for consumption. The explanatory power of country factors increases as variances are
driven more by country and idiosyncratic factors than by the world factor[23]. This
con?icts with the results from studies of output growth ?uctuations. The cause of these
differences is an important area for further research.
A static factor model provides a description of the variance-covariance matrix of a
set of random variables, while a dynamic factor model provides a description of the
inter-periods correlations. Therefore, the dynamic factors can describe
contemporaneous and temporal covariance among the variables (Kose et al., 2008a, b).
Compared with correlation approaches, an important advantage of the dynamic
factor model is that it allows for the separation of idiosyncratic components and common
comovements of global, regional, country-speci?c, and idiosyncratic factors. It is well
suited to investigate the degree of region-wide comovements and to study the joint
properties of ?uctuations in output and its components. However, the disadvantage of
dynamic factor models is that they need relatively long time series and it is easy to lose
degrees of freedom. In addition, it cannot be used to analyze bilateral comovements
between concerned countries.
4. Concluding remarks: the consistency of measurements
Empirical studies of ?nancial and economic interdependence have provided
considerable useful information. For example, they ?nd that most national economies
and ?nancial markets are substantially in?uenced by international in?uences, but also
that these are generally not as strong as to completely dominate the national
performance. Often such studies ?nd that reality lies well within the range of the extreme
popular opinions offered about such issues as decoupling and recoupling and contagion.
However, there is still considerable disagreement among experts about the strengths of
some important forms of interdependence.
It is not surprising that different studies often do not exactly agree. More troubling is
that they sometimes fundamentally disagree. In part this is no doubt because some
experts have strong beliefs about the nature of certain relationships and interpret their
results in this light. But we also ?nd that not only the use of different sets of countries
and time periods but also different estimation techniques sometimes yield substantially
different conclusions[24].
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The differences in results from different data sets should remind us that economic
relationships are generated by the behavior of human beings and thus can vary from
one situation to another. While we are able to capture some fairly strong regularity,
these often do not have the consistency of the physical laws of nature. The differences
from identical data sets resulting from different estimation techniques such as were
illustrated at the end of the previous section need to be the focus of extensive research.
In the meanwhile where alternative approaches have been recommended we should
check the sensitivity of the results to the different methods. Obviously we should have
more con?dence when the results of doing this roughly agree than when they differ
substantially.
Notes
1. See the analysis and references in Sula and Willett (2009) and the companion paper in part
one of this special issue by Efremidze et al. (2011).
2. See, for example, the general discussion of OCA criteria and references in Willett (2003a, b)
and applications to Asia and Europe in Willett et al. (2010a, b).
3. For analysis and references, see Willett et al. (2010).
4. For further more discussion and references, see Liang and Willett (2008).
5. See, for example, Willett et al. (2005) and Eichengreen et al. (1996).
6. Some of the other techniques used are probit analysis for studying the probability of
effects of the spread of crises (Angkinand et al., 2009) and principal components analysis
for identifying the common factors of the spread of the crisis (for detailed analysis, see
Forbes and Rigobon (2001)). They are not necessarily completely exclusive from methods
we discuss in this paper though. For example, the probit method is often used in panel
analysis.
7. The mathematical presentation of the simple correlation is as the following:
r
X;Y
¼ corrðX; YÞ ¼
cov ðX; YÞ
s
X
s
Y
¼
E½ðX 2m
X
ÞðY 2m
Y
Þ?
s
X
s
Y
where X and Y are two variables of which the relationship is to be evaluated, m
X
and m
Y
are expected values for X and Y, respectively, s
X
and s
Y
are their standard deviations, and
E is the expected value operator.
8. For stock markets it is important to consider whether they should be measured in dollars or
the domestic currency for the speci?c question being investigated. For example, for portfolio
allocations by US investors the dollar value is most relevant while for looking
at the sensitivity of a foreign market to a US shock the domestic currency value is more
relevant.
9. For an application to the measurement of business cycle correlations and their implications
for OCA analysis, see Willett et al. (2010).
10. For discussion of how the HP ?lters are estimated, see Appendix A of the longer version of
this paper that appears on the web site of the Claremont Institute for Economic Policy
Studies: www.cgu.edu/pages/1380.asp. For evaluation of the HP ?lter, see Ravn and Uhlig
(2002).
11. See Appendix B in the longer version of this paper.
12. For more evaluation of DCC-GARCH, see Engle and Kevin (2001) and Vargas (2006).
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13. The frequency approach is based on time varying coherence to detect endogenously
structural changes in the comovement process. This method not only detects comovement
dynamics in different cycles, but also tests if these countries tend to be more synchronized
or not. The coherence is interpreted as the squared linear correlation coef?cient for each
frequency of the spectra of two series. When calculating the time varying coherence,
they employ the Bai and Perron (1998) test to determine endogenously break dates because
the choice of this type of model is motivated by TVCF characteristics.
14. For more econometric details of the TVCF analysis, see Essaadi and Boutahar (2008).
15. The short-run effects are measured by a vector-correction model and the long-run effects
are measured by conintegration tests, for example, Johansen (1991) procedure.
16. While standard OLS assumes a simple linear relationship among the variables,
quartile analysis allows the estimation of different coef?cients for different parts of the
distributions.
17. The reduced form presentation of the VAR model is as the following:
Yt ¼ c þ A1Yt 21 þ A2Yt 22 þ · · · þ ApYt 2p þ et
where Yt is a set of k time series variables: Yt ¼ (Y1t, Y2t. . .Ykt)
0
, the Ai s are k £ k
matrices of coef?cients, c is a k £ 1 vector of constants, p is the order or lag of the model, and
et is a k £ 1 vector of error terms – the et s are serially uncorrelated but may
be contemporaneously correlated.
18. GMM is a generic method to estimate parameters when the full shape of the distribution
function of the data is not known and the parameters of interest are ?nite-dimensional.
19. The restrictions de?ne the endogenous relationship among variables and can be realized by
multiplying a control matrix determining the order of the effects.
20. More applications of factor analysis can be found in studies by Otrok and Whiteman (1998),
and Bernanke et al. (2005).
21. The pioneering use of this approach to attempt to distinguish real versus nominal shocks is
by Blanchard and Quah (1989).
22. For technical details see Appendix C of the longer version of this paper.
23. Li also ?nds that regional factors and country factors also play a more important role in
explaining gross import ?uctuations than in explaining gross exports (Li, 2011).
24. Of course they also often ?nd similar results. For example, both Li (2011) and Zhang (2011a)
?nd that the use of linear versus HP trends makes little difference in their studies of
macroeconomic and stock market interdependencies.
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About the authors
Linyue Li (PhD, Claremont Graduate University) serves as the Research Associate at the
Claremont Institute for Economic Policy Studies.
Nan Zhang (PhD, Claremont Graduate University) serves as the Research Associate at the
Claremont Institute for Economic Policy Studies and is also a Senior Research Analyst at the
Milken Institute.
Thomas D. Willett (PhD, University of Virginia) serves as Director of the Institute for
Economic Policy Studies and is also Horton Professor of Economics at Claremot Graduate
University and Claremont McKenna College. Thomas D. Willett is the corresponding author and
can be contacted at: [email protected]
Measuring
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